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Trajectories of change in depression severity during treatment with antidepressants

Published online by Cambridge University Press:  29 October 2009

R. Uher ,
B. Muthén ,
D. Souery ,
O. Mors ,
J. Jaracz ,
A. Placentino ,
A. Petrovic ,
A. Zobel ,
N. Henigsberg  and
M. Rietschel
...Show all authors
R. Uher*
Affiliation:
Medical Research Council (MRC) Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, UK
B. Muthén
Affiliation:
University of California, Los Angeles, CA, USA
D. Souery
Affiliation:
Laboratoire de Psychologie Médicale, Université Libre de Bruxelles and Psy Pluriel – Centre Européen de Psychologie Médicale, Bruxelles, Belgium
O. Mors
Affiliation:
Aarhus University Hospital, Risskov, Denmark
J. Jaracz
Affiliation:
Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poland
A. Placentino
Affiliation:
Biological Psychiatry Unit and Dual Diagnosis Ward IRCCS, Centro San Giovanni di Dio, FBF, Brescia, Italy
A. Petrovic
Affiliation:
Institute of Public Health, Ljubljana, Slovenia
A. Zobel
Affiliation:
Rheinische Friedrich-Wilhelms-Universitaet Bonn, Germany
N. Henigsberg
Affiliation:
Croatian Institute for Brain Research, Medical School, University of Zagreb, Croatia
M. Rietschel
Affiliation:
Central Institute of Mental Health, Division of Genetic Epidemiology in Psychiatry, Mannheim, Germany
K. J. Aitchison
Affiliation:
Medical Research Council (MRC) Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, UK
A. Farmer
Affiliation:
Medical Research Council (MRC) Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, UK
P. McGuffin
Affiliation:
Medical Research Council (MRC) Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, UK
*
*Address for correspondence: Dr R. Uher, Medical Research Council (MRC) Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King's College London, UK (Email: [email protected])
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Abstract

Background

Response and remission defined by cut-off values on the last observed depression severity score are commonly used as outcome criteria in clinical trials, but ignore the time course of symptomatic change and may lead to inefficient analyses. We explore alternative categorization of outcome by naturally occurring trajectories of symptom change.

Method

Growth mixture models were applied to repeated measurements of depression severity in 807 participants with major depression treated for 12 weeks with escitalopram or nortriptyline in the part-randomized Genome-based Therapeutic Drugs for Depression study. Latent trajectory classes were validated as outcomes in drug efficacy comparison and pharmacogenetic analyses.

Results

The final two-piece growth mixture model categorized participants into a majority (75%) following a gradual improvement trajectory and the remainder following a trajectory with rapid initial improvement. The rapid improvement trajectory was over-represented among nortriptyline-treated participants and showed an antidepressant-specific pattern of pharmacogenetic associations. In contrast, conventional response and remission favoured escitalopram and produced chance results in pharmacogenetic analyses. Controlling for drop-out reduced drug differences on response and remission but did not affect latent trajectory results.

Conclusions

Latent trajectory mixture models capture heterogeneity in the development of clinical response after the initiation of antidepressants and provide an outcome that is distinct from traditional endpoint measures. It differentiates between antidepressants with different modes of action and is robust against bias due to differential discontinuation.

Keywords

Antidepressant responsedepressiongrowth mixture modellingrandomized controlled trials
Type
Original Articles
Information
Psychological Medicine , Volume 40 , Issue 8 , August 2010 , pp. 1367 - 1377
Copyright
Copyright © Cambridge University Press 2009

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References

Bandelow, B, Baldwin, DS, Dolberg, OT, Andersen, HF, Stein, DJ (2006). What is the threshold for symptomatic response and remission for major depressive disorder, panic disorder, social anxiety disorder, and generalized anxiety disorder? Journal of Clinical Psychiatry 67, 1428–1434.CrossRefGoogle ScholarPubMed
Bech, P, Allerup, P, Reisby, N, Gram, LF (1984). Assessment of symptom change from improvement curves on the Hamilton depression scale in trials with antidepressants. Psychopharmacology (Berlin) 84, 276–281.CrossRefGoogle ScholarPubMed
Beck, AT, Ward, CH, Mendelson, M, Mock, J, Erbaugh, J (1961). An inventory for measuring depression. Archives of General Psychiatry 4, 561–571.CrossRefGoogle ScholarPubMed
Beunckens, C, Molenberghs, G, Verbeke, G, Mallinckrodt, C (2008). A latent-class mixture model for incomplete longitudinal Gaussian data. Biometrics 64, 96–105.CrossRefGoogle ScholarPubMed
Carmody, TJ, Rush, AJ, Bernstein, I, Warden, D, Brannan, S, Burnham, D, Woo, A, Trivedi, MH (2006). The Montgomery Asberg and the Hamilton ratings of depression: a comparison of measures. European Neuropsychopharmacology 16, 601–611.CrossRefGoogle ScholarPubMed
Cohen, J (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46.Google Scholar
Fahndrich, E (1984). The arbitrariness of response definition in clinical trials with antidepressants. Pharmacopsychiatry 17, 107–108.CrossRefGoogle ScholarPubMed
Frank, E, Prien, RF, Jarrett, RB, Keller, MB, Kupfer, DJ, Lavori, PW, Rush, AJ, Weissman, MM (1991). Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Archives of General Psychiatry 48, 851–855.CrossRefGoogle ScholarPubMed
Gueorguieva, R, Krystal, JH (2004). Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Archives of General Psychiatry 61, 310–317.CrossRefGoogle ScholarPubMed
Hamilton, M (1967). Development of a rating scale for primary depressive illness. British Journal of Clinical Psychology 6, 278–296.Google ScholarPubMed
Joyce, PR, Mulder, RT, Luty, SE, Sullivan, PF, McKenzie, JM, Abbott, RM, Stevens, IF (2002). Patterns and predictors of remission, response and recovery in major depression treated with fluoxetine or nortriptyline. Australian and New Zealand Journal of Psychiatry 36, 384–391.CrossRefGoogle ScholarPubMed
Lane, P (2008). Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches. Pharmaceutical Statistics 7, 93–106.CrossRefGoogle ScholarPubMed
Leucht, S, Heres, S, Hamann, J, Kane, JM (2008). Methodological issues in current antipsychotic drug trials. Schizophrenia Bulletin 34, 275–285.CrossRefGoogle ScholarPubMed
Mallinckrodt, CH, Clark, WS, David, SR (2001). Accounting for dropout bias using mixed-effects models. Journal of Biopharmaceutical Statistics 11, 9–21.CrossRefGoogle ScholarPubMed
Montgomery, SA (1994). Clinically relevant effect sizes in depression. European Neuropsychopharmacology 4, 283–284.CrossRefGoogle Scholar
Montgomery, SA, Asberg, M (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry 134, 382–389.CrossRefGoogle ScholarPubMed
Mulder, RT, Joyce, PR, Frampton, C (2003). Relationships among measures of treatment outcome in depressed patients. Journal of Affective Disorders 76, 127–135.CrossRefGoogle ScholarPubMed
Muthén, B, Asparouhov, T (2008). Growth mixture modeling: Analysis with non-Gaussian random effects. In Longitudinal Data Analysis (ed. Fitzmaurice, G., Davidian, M., Verbeke, G. and Molenberghs, G.), pp. 143–165. Chapman, all/CRC Press: Boca Raton.Google Scholar
Muthén, B, Brown, H, Leuchter, A, Hunter, A (2008). General approaches to analysis of course: Applying growth mixture modeling to randomized trials of depression medication. In Causality and Psychopathology: Finding the Determinants of Disorders and their Cures (ed. Shrout, P. E.). American Psychiatric Publishing: Washington, DC.Google Scholar
Muthén, B, Muthén, L. (2008). Mplus User's Guide. Muthén, Muthén: Los Angeles, CA.Google Scholar
Papakostas, GI, Crawford, CM, Scalia, MJ, Fava, M (2007). Timing of clinical improvement and symptom resolution in the treatment of major depressive disorder. A replication of findings with the use of a double-blind, placebo-controlled trial of Hypericum perforatum versus fluoxetine. Neuropsychobiology 56, 132–137.Google Scholar
Parker, G (2009). Antidepressants on trial: how valid is the evidence? British Journal of Psychiatry 194, 1–3.CrossRefGoogle ScholarPubMed
Prien, RF, Carpenter, LL, Kupfer, DJ (1991). The definition and operational criteria for treatment outcome of major depressive disorder. A review of the current research literature. Archives of General Psychiatry 48, 796–800.Google Scholar
Quitkin, FM, Rabkin, JG, Ross, D, Stewart, JW (1984). Identification of true drug response to antidepressants. Use of pattern analysis. Archives of General Psychiatry 41, 782–786.Google Scholar
Rietschel, M, Kennedy, JL, Macciardi, F, Meltzer, HY (1999). Application of pharmacogenetics to psychotic disorders: the first consensus conference. The Consensus Group for Outcome Measures in Psychoses for Pharmacological Studies. Schizophrenia Research 37, 191–196.CrossRefGoogle Scholar
Royston, P, Altman, DG, Sauerbrei, W (2006). Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in Medicine 25, 127–141.CrossRefGoogle ScholarPubMed
Stassen, HH, Angst, J, Hell, D, Scharfetter, C, Szegedi, A (2007). Is there a common resilience mechanism underlying antidepressant drug response? Evidence from 2848 patients. Journal of Clinical Psychiatry 68, 1195–1205.Google Scholar
StataCorp (2007). Stata statistical software: release 10. Stata Corp LP: College Station, TX.Google Scholar
Streiner, DL (2002). Breaking up is hard to do: the heartbreak of dichotomizing continuous data. Canadian Journal of Psychiatry 47, 262–266.CrossRefGoogle Scholar
Szegedi, A, Jansen, WT, Willigenburg, AP Pv, van der, ME, Stassen, HH, Thase, ME (2009). Early improvement in the first 2 weeks as a predictor of treatment outcome in patients with major depressive disorder: a meta-analysis including 6562 patients. Journal of Clinical Psychiatry 70, 344–353.CrossRefGoogle ScholarPubMed
Szegedi, A, Muller, MJ, Anghelescu, I, Klawe, C, Kohnen, R, Benkert, O (2003). Early improvement under mirtazapine and paroxetine predicts later stable response and remission with high sensitivity in patients with major depression. Journal of Clinical Psychiatry 64, 413–420.CrossRefGoogle ScholarPubMed
Taylor, MJ, Freemantle, N, Geddes, JR, Bhagwagar, Z (2006). Early onset of selective serotonin reuptake inhibitor antidepressant action: systematic review and meta-analysis. Archives of General Psychiatry 63, 1217–1223.CrossRefGoogle ScholarPubMed
Uher, R (2008). The implications of gene-environment interactions in depression: will cause inform cure? Molecular Psychiatry 13, 1070–1078.Google Scholar
Uher, R, Farmer, A, Maier, W, Rietschel, M, Hauser, J, Marusic, A, Mors, O, Elkin, A, Williamson, RJ, Schmael, C, Henigsberg, N, Perez, J, Mendlewicz, J, Janzing, JG, Zobel, A, Skibinska, M, Kozel, D, Stamp, AS, Bajs, M, Placentino, A, Barreto, M, McGuffin, P, Aitchison, KJ (2008). Measuring depression: comparison and integration of three scales in the GENDEP study. Psychological Medicine 38, 289–300.CrossRefGoogle ScholarPubMed
Uher, R, Huezo-Diaz, P, Perroud, N, Smith, R, Rietschel, M, Mors, O, Hauser, J, Maier, W, Kozel, D, Henigsberg, N, Barreto, M, Placentino, A, Dernovsek, MZ, Schulze, T, Kalember, P, Zobel, A, Czerski, P, Larsen, ER, Souery, D, Govannini, C, Gray, JM, Lewis, CM, Farmer, A, Aitchison, KJ, McGuffin, P, Craig, I (2009 a). Genetic predictors of antidepressant response in the GENDEP project. Pharmacogenomics Journal 9, 225–233.CrossRefGoogle ScholarPubMed
Uher, R, Maier, W, Hauser, J, Marusic, A, Schmael, C, Mors, O, Henigsberg, N, Souery, D, Placentino, A, Rietschel, M, Zobel, A, Dmitrzak-Weglarz, M, Petrovic, A, Jorgensen, L, Kalember, P, Govannini, C, Barreto, M, Elkin, A, Landau, S, Farmer, A, Aitchison, KJ, McGuffin, P (2009 b). Differential efficacy of escitalopram and nortriptyline on dimensional measures of depression in the GENDEP project. British Journal of Psychiatry 194, 252–259.CrossRefGoogle Scholar
Vandenbroucke, JP (2008). Observational research, randomised trials, and two views of medical science. PLoS Medicine 5, e67.CrossRefGoogle ScholarPubMed
Wing, JK, Sartorius, N, Ustin, TB (1998). Diagnosis and Clinical Measurement in Psychiatry. A Reference Manual for SCAN. World Health Organization: Geneva.Google Scholar
Zimmerman, M, Posternak, MA, Chelminski, I (2004). Derivation of a definition of remission on the Montgomery-Asberg depression rating scale corresponding to the definition of remission on the Hamilton rating scale for depression. Journal of Psychiatric Research 38, 577–582.CrossRefGoogle Scholar
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